{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Matplotlibs属于画图库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as  pd\n",
    "import matplotlib.pyplot as plt #目前画图我们只需要导入pyplot库【以后很多库都是对于matplotlib的封装】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#此指令目的让在notebook上直接打印图，而不要每次出图plt.show()\n",
    "%matplotlib  inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最基本的一个图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'ylabel')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SUtJYvHkvRQ7iGlTnlnNaM6RrLN2b1dG4PiISslQEp2nb3iM/nd27dOt+ANrF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\n5v4XM713+NBhcnUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaac6e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,5],[1,4,9,16,25])# 添加x轴和y抽\n",
    "plt.xlabel('xlabel',fontsize=16)# 添加x标签 可以调整大小\n",
    "plt.ylabel('ylabel')# 添加y标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画不同的线条字符标识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "字符|类型 | 字符|类型\n",
    "---|--- | --- | ---\n",
    "`  '-'\t`| 实线 | `'--'`|\t虚线\n",
    "`'-.'`|\t虚点线 | `':'`|\t点线\n",
    "`'.'`|\t点 | `','`| 像素点\n",
    "`'o'`\t|圆点 | `'v'`|\t下三角点\n",
    "`'^'`|\t上三角点 | `'<'`|\t左三角点\n",
    "`'>'`|\t右三角点 | `'1'`|\t下三叉点\n",
    "`'2'`|\t上三叉点 | `'3'`|\t左三叉点\n",
    "`'4'`|\t右三叉点 | `'s'`|\t正方点\n",
    "`'p'`\t| 五角点 | `'*'`|\t星形点\n",
    "`'h'`|\t六边形点1 | `'H'`|\t六边形点2 \n",
    "`'+'`|\t加号点 | `'x'`|\t乘号点\n",
    "`'D'`|\t实心菱形点 | `'d'`|\t瘦菱形点 \n",
    "`'_'`|\t横线点 | |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'ylabel')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ogK+ISG30J7KISIJTEYiIJLhQisDMhpvZfDNbZGa3h5FBREQiYl4EZpYMPAqM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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaae6c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,5],[1,4,9,16,25],'-.')# 添加线条类型\n",
    "plt.xlabel('xlabel',fontsize=16)\n",
    "plt.ylabel('ylabel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "颜色\n",
    "表示颜色的字符参数有：\n",
    "\n",
    "字符 | 颜色\n",
    "-- | -- \n",
    "`‘b’`|\t蓝色，blue\n",
    "`‘g’`|\t绿色，green\n",
    "`‘r’`|\t红色，red\n",
    "`‘c’`|\t青色，cyan\n",
    "`‘m’`|\t品红，magenta\n",
    "`‘y’`|\t黄色，yellow\n",
    "`‘k’`|\t黑色，black\n",
    "`‘w’`|\t白色，white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'ylabel')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3WhaRuKJyiKKF6xcy6p1RvDLoFSqVqUSrWq24ucPN9G/R/6cRUEVE4pHKoQh9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\nEBGRXFQOIiKSi8pBRERy+T8JJOLfWng6mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb2d9dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,5],[1,4,9,16,25],'-.',color='g')# 添加颜色\n",
    "plt.xlabel('xlabel',fontsize=16)\n",
    "plt.ylabel('ylabel',fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#或者换种方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'ylabel')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9eweaLkmr2aoph43rNw40XZJWs1VTDts2b2Pd2nX3mbZu7Tq2bd7WUyJJGl2rphy2bNrC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\nDpKkDstBktRhOUiSOiwHSVKH5SBJ6vj/7OOHdqbhsasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc31abe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,5],[1,4,9,16,25],'go')# 添加颜色\n",
    "plt.xlabel('xlabel',fontsize=16)\n",
    "plt.ylabel('ylabel',fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 绘制多条线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#例如绘制三条线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_ndarray=np.arange(0,10,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc3c9e80>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xafe5828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(temp_ndarray,temp_ndarray,'r-')\n",
    "plt.plot(temp_ndarray,temp_ndarray**2,'g-.')\n",
    "plt.plot(temp_ndarray,temp_ndarray**3,'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#或者可以简写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc428748>,\n",
       " <matplotlib.lines.Line2D at 0xc428908>,\n",
       " <matplotlib.lines.Line2D at 0xc432128>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fH8D7kBfGtlXRZWZFwGeAu2Pdlmgzs2xgIbAWwDl31DnXHNtWRV0KkG5mKUAG\nsDPG7Rm34jnoB7sJeUIFXTczKwPmAi/HtiVRdwfwbSAU64bEQDnQBNzjD13dbWaZsW5UtDjnGoEf\nA/XALqDFOfd0bFs1fsVz0A9+d+4EY2ZZwK+BrzvnWmPdnmgxswuBPc65DbFuS4ykAGcCdznn5gKH\ngITZT2VmeXj/wc8EZgCZZvbF2LZq/IrnoG8AivvMF5Fg/7qZ2QS8kK9xzj0W6/ZE2QLgIjPbjjds\nd76ZPRDbJkVVA9DgnOv+L+5RvOBPFEuAbc65JudcB/AY8PEYt2nciuegH/ObkI9nZmZ447ObnXM/\niXV7os05d5Nzrsg5V4b3u1/vnEuYHp1z7n1gh5nN9osWA5ti2KRoqwfmm1mG/1lYTALtjI5USqwb\ncLycc51m9n+AP+Dtcf+lc+7NGDcrmhYAlwF/NbO/+GU3O+d+F8M2SXRdB9T4HZ2twJdi3J6occ69\nbGaPAq/iHYH2GjpDdkg6M1ZEJODieehGRETCoKAXEQk4Bb2ISMAp6EVEAk5BLyIScAp6EZGAU9CL\niAScgl5EJOD+P5eJ3U6FSJH8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xafe5c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(temp_ndarray,temp_ndarray,'r--',\n",
    "         temp_ndarray,temp_ndarray**2,'g-.',\n",
    "         temp_ndarray,temp_ndarray**3,'bo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 指定宽度线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc489278>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MvKKxuaXIIhDgzGcNQ28Gn4TtzSeS6BpIJS2td0lVVTPrakuh3TNPDU1iZt4f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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc441908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(10,-10)\n",
    "y=np.sin(x)\n",
    "plt.plot(x,y,linewidth = 3.0)#指定线条粗细"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc4efc18>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f4yQr5Kkq0/7l3nji6lJS4o2Ik2XLdJKPRfEEjuA5+7GPuDgdfHHFFU5LYj1N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\noxgMBoPBUAqjGAwGg8FQiv8HEdrtxuo+oLAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4b2668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,color='b',linestyle=':',marker='*',markerfacecolor='r',markersize=12)#指定线条粗细 liststyle【线的类型】 marker[描点类型]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#我们也可以先画出图来，在进行坐标设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f4yQr5Kkq0/7l3nji6lJS4o2Ik2XLdJKPRfEEjuA5+7GPuDgdfHHFFU5LYj1N\nm+qQXLcRmMvz/cZa/PnPkLHefbk8JvMZaJB4hCGFE/i60zAWrY6OIvVLlkD37jB7Nlx5pdPSlE9e\nno4zd1O/gnD425gSJjy9hy3HTwbmtUnJ5IHnGvGHR10anmSwjWED8+h8ZfLPYdvLl+vv/4UXwtTJ\nJSz/6kREw7ZtzXz2EsHswfG4N564utSpo+v0+HvdupXU1OhRClDafnxmvDdqQe3fD0XRs1h2NWVz\nee6+W/cDB5fl8oRib3LbFo6Pwav24Ghm9GiRt95yWorwCbQfNz3jmDz0kP6u2WU/rg4zZuiv/erV\nTktiHbNni5x5pnjif3fZMh2ZZxfY6WPwEl61B1eVY8e84WMAmD4d/vtfp6UIn0D78aafavHqq86W\nOg+Fzp1hzBioV89pSayjXj246irtZ3A7nTu7c1Uf0z6GaLYHn3UWXHopvPWW05JUTlGRO+vZVBW/\n/bj/HXHUqFE6DNcO+7HBe2Rnw5w5ugqvhXm75WJ8DCHgtwdPVoNpkezSeOJq8tBD7i2FUZZoUApw\n0n78xhu6oueBAyePucp+XIZjx2D1avf2BqgqhYVOSxA6W7bAHXe4r8tczCqGwNaXY9LGMW5aI152\nqPVlJHjoIehpa63a6rNxo3aUb9zotCTWkJ4OjzwCdes6LUlo9O8PvXrB1PeSWLLEaWnCQwSaNNF1\noLzABRfoWmHduzstSWliVjGU7Q1Qty48/KS77cGhcugQHD7stBShU1iofQy7dzstiTV07ap7+rqx\nc1gw/vAH6HxhdOTyFBV5KzcjOVn7Pd0WmReziqFsb4B334U//Sk6egNMmKDDVXPdV+Y9KJ066YZJ\ndnWxijRZWd5x/IN21O7e6N7eAFUhIQGefRauv95pSUJn4UKYOtVpKUoTs4qhbDzxX/4C27fr5262\nB4fCDTfA66/rHs8Gezl6VEfDvPxy5WOdoqLaTl7P5cnKggH9veUree89+OMfnZaiNDGrGMrSoIE7\nWwBWhwsvhAcecFqKqvH669rP4HWU0iu2665zWpLyCdYbYE+he3sDVIWhQ+Ht6Ul8843TkoTOCy/A\njz86LUVpjGLwceKEvsv7+munJQkPEd3o3Ut3TAAHD2ofg5dMMME47TT94/SLXzgtSflEcy5PrWTt\nK/nfPO/4SurXd9/q3igGHwkJ8PTTOqbYy/z0k14xeCF/IZARI/S194rDtjx27PBGQcCaNbU/bcjI\n5vRKKf2l75UyhyEjm//sf/MS29d6z1dy/DiMGuWum1KjGHzEx8O+fTBypNOShMcZZ8AHH3jL+RZN\nPPooXH6501KETmAuT1qSt3L8f9oFAAAgAElEQVR5osVXkpiob4zmzXNakpPEdOazwT0cP67zLvr3\n1+0+vcrixbooXa9eTktSOVlZ0LZFAZerb1hfK51dB2tx1pm5tMxeziLpytadiTRu7LSU5bN2Ldza\nI5fO+z5n/InB1ORU+2kuqQxLnkJGox7MmO1es1hOjj0+Tlszn5VS1yulNiqltiilhgc5nqSUet93\nfJlSqmXAsSd8+zcqpRx12S1ZAsOGebuj1cqVsGGD01JUnZQU/eh1U1LXrt5QClA6l2f+slp8+y0s\nXe2dXJ5o8pW4LvAllEp7FW1APLpzW2sgEd2NrWOZMcOAN3zP+wHv+5539I1PAlr5zhNf2ZxWd3Dz\n869/idSubW+1Q6u56iqRLl2cliI2OXxYZOlSkbw8pyUJjaEDcmXKpOB9hqdMKpahA3Jtlqj6ONlH\n2QoWLxa5/36R/PzIzoON1VU7A1tEZJuIFADTgbL3TL0Avzv0Q+BqpZTy7Z8uIvki8iOwxXc+R+jf\nX0fHuLHaYSjk5sLpyfm89JLTksQmixbBJZfoqDAvUDaXZ84cWLBAP/daLs+urQXcWKR9Ja1SvdEH\nI5Bt23Tv+awspyXRWKEYzgR2Bbze7dsXdIyIFAFHgHohvtc24uO9bcpYsgQ++SKJ4mKnJakeH34I\nrVtr5exFLrkEZs2C885zWpLqMXw4/PWvTktRdfx1z1YVduTxlHGMfbcRozxW9+z223XRxRYtnJZE\nY4ViCPZTWtajXd6YUN6rT6DUfUqpDKVURnYE4wFHjoTRoyN2+ojy2Uwdw/3lF96J4Q6kUSPo0kU7\nor1IvXpw880utBeHyPTpOgvXa/h9Jam/6sIHs2vRu7f7+2CUJS7OXTelViiG3UDzgNfNgMzyxiil\nagBnAAdDfC8AIjJRRNJFJL1BBAuXf/+9LkHsRebO8sVwf+adGO5ArrgCpk2DMx1bM4bHnDnerhDb\nrp13KsIG4q97Nnt+Ct266X3+PA0v1T179lndNMkNWKEYvgXaKaVaKaUS0c7lWWXGzAIG+J73Aeb7\nHCGzgH6+qKVWQDtguQUyVZtPPvFGcliwGO6D+3QM9+4d3onhDkZOjjd7A9x5p3dXmwB798Jrr8HO\nnU5LUjUmTE2l21VxfPUVFJRxKXjJV7JihQ7BdQNht0gRkSKl1IPAl+gIpckislYpNRLtAZ8FTAL+\npZTagl4p9PO9d61SagawDigCHhARj1rI7WXECymsXlkmhtv3T5FVoPs0BsZwj3jBG/aNnj112fBF\ni5K4c7C3Kq7Om+e+8slVYd8++N3voGFDSEtzWpqqMX06PPEEHDni3c/g00+dluAkluQxiMgXInKW\niLQRked9+0b4lAIickJEbhWRtiLSWUS2Bbz3ed/72ovIv62QJxw2b9Z24uWOrlsqJ5piuAO58kqo\nmeTN3gCdOumWql6lQwftyO3b12lJqs6gQTB3Lpx+utOSRAemJEYZkpN1vRsvNLqJxno3//d/kLPX\ne/VufvhBR1WVNWV4iRo1oHFjdzlBQ6VRI2+tLoOxaRPcdBN8+63TkhjFcArNm8OqVe5rtVcRu7YW\ncJOv3k3LFG/FcAfzlWzd5L16N9Om6d69cR7/j/rPf3RBNy8hAm+/rXMBvExKiq4wfOSI05IQfuaz\nE1ukMp+9SGamSGpCvnRPXCAdWxyTmTNFOqTlSPfE+ZKakC9ZWU5LWDFr1mh5ByRPlxxSS2Wu+rcc\nUuXu5PelY4tjsmaN0xIHJydHZO1ap6UInz/8QaR+fZGSEqclCZ3MTP1Vee01pyVxP9iY+Rx1jB3r\n7kYrgfhjuOMu7cJ9j9SiVy9vxXBHi6+kZk3o2LHycW7nL3/R0UleMic1bKjNMLfd5rQk0YNRDEFI\nTtb/6CUeCH/2x3A3bZPCmDH6H9prMdxe95UUFmrzy7p1TksSPikp3jOHxcfrHIwIpjfZxt/+5o6b\nUo99Bexh6FD4+GNv/IP4693885+n1ujxUgw3lO4N4CVfyfbt8PjjEA2V4AsL9d/y2WdOSxI6X3yh\n/1+jgZQUnTnv9E2p6cdgcAWBvQFWJ6dz4EQt2jTOpfkeb/QGOHxYR/V4tRyGHxGdef7b38Izzzgt\nTWhcd52ur+WGaB4ryM2FB+/LZ/ybSaRafF9naz+GaKOoSLfHfPllpyUJjT17dJinF/sw+AnsDfD2\nR7V45RVYvNI7vpLatb2vFECbInfv9o5SAF248JNPnJbCOpYsganvJbFkiXMyGMUQhBo14NxzoUkT\npyUJja1b4fXXtdPQq/h9JVOmp3DNNdqcV6eON3wlb78Nkyc7LYV1eMGEGkhSknfra5WlqAgG3OF8\ngqcxJUUJ/lLb8fHOymEV+/Zpe7fb/+Fzc6Fj23zS2ia5qpl7OCxaBK+8Av/8p14JuZlt23RF2EGD\n3P9dCZWWdY7Q//AEvu40jEWrz7D03MaUZAH+QHovEB8fPUoBdHmJZ591WorKWbIEdu5J4vHHnZbE\nOo4e1Ume+/Y5LUnlrFwJTz/tkqSwahAswTM/z/kET6MYyuHDD/Xd0k8/OS1J5Tz1lL67iybGj4d7\n73VaispZOFcv+5ct9lZdp4ro0UPnBXih7tOvf62r8bZv77Qk1WPECyl0SMtlQPL75FATQZFVUI/6\nHCCroB6CIoea3J08g44tchjxQrItchnFUA5t2sBddzktRWj873+6j0Q0ceut0NmxJq+h85+PdF2n\n/870Tl2naKNmTe+ult2a4GkUQzlccIFuc/j04+7vC/D11/oOO5rIy4OlS/XdoFsItuzftV0v+3f8\n6J26TqHwxBPw2GNOS1E5Tz3l/RwGNyZ4GsVQAW4IGwsVL5UwCIXFi+HSS90Vmx5s2b+3SC/79zi4\n7I8ER4/qzc2IwLvv6huIaMCf4DlFDaZVqsMJnqEUVHLbZlcRvTYtCqUmx+Sp4YW2zFcdPv5Y5Lbb\nRI4edVoSazl0SGTWLJEDB5yWpDQ5OSID+uZJh9TtsoaOpYr9reYc6ZC6XQbelic5OU5LGjsUFTkt\nQfgEK4Z5dnPri2FiRxE9pVRdpdQcpdRm32OdIGPOV0otUUqtVUqtUkrdFnBsqlLqR6XUSt92fjjy\nWE1Skfv7Auzfr3tUu7WOUHWpXVs3THJbD2I3LvtjHa/6FwIJTPBcvrYWb70FNRs6l+AZrilpODBP\nRNoB83yvy5IH3C0i5wDXA68opQKjox8TkfN928ow5ak2QXsoZzsfNlYZ996r+8R6LSkpFDZs0F25\n3Ii/B8YkBtM8wTt1nUJl+3a45BL4t+M9Fctnxgy4/36dFOZ1AhM8a9bUXfTuvdfBBM9QlhXlbcBG\noInveRNgYwjv+QFo53s+FehT1XkjYUqKlr4A0cSAASJNmjgtxal4vQdGKBw5IvKrX4n85z9OS1I+\nL7wgcvbZTkvhLbCpH0MjEcnyKZgsoGFFg5VSnYFEYGvA7ud9JqaxSqmkMOWpNm4NG6uI3Fzo0sVd\nTcSt5IknYM6cysfZTdllf+/e3uqBEQqnnw7z57ujBHR5PPEErF/vtBSR48gR5xL3KlUMSqm5Sqk1\nQbZeVZlIKdUE+BcwSET866IngLOBi4G6QLn5o0qp+5RSGUqpjOzs7KpMHTJesx8fPqz/gRMTnZYk\nMrRvjysUcFn8y/6OF6Vw//16n9d6YBjczaFD2s/mVOJqpYpBRK4RkU5Btk+Bvb4ffP8Pf9AkeqXU\n6cBs4CkRWRpwbv+iOx+YApSb0iQiE0UkXUTSG0S4I4erwsYq4Mwz9R31DTc4LUlkKCzUGehl+0w4\njb8HxokTcPx46WNe64FREX/7G7RtC4Nud18uz/79ui/7ggVOSxIZ6tTRnSSvusqZ+cM1Jc0CBvie\nDwBOMWoopRKBmcDbIvJBmWN+paKA3sCaMOUJm6wsmPjPONbRkdFp43hpciOeb/wa6+jAP96MY88e\npyWMHeLi4M47day6G3n6aa24opWWLXVZjKnT3JfLc/Cgvqt2uqFNJHn4YZ1o6wThKoaXgGuVUpuB\na32vUUqlK6X8i6C+wJXAwCBhqe8qpVYDq4H6wF/ClCdsytqPf/wRtmTVpP6N7rMfDxmifzijlfh4\nvVoYOdJpSWKTX/8aLjzP+RLQwTjrLJ38ePXVTksSOY4f199/Jwp5hqUYROSAiFwtIu18jwd9+zNE\nZIjv+TsikiAnQ1J/DksVkatE5FyfaepOEXG8AELZsLHevXVY3JvvuM9+nJam7+qimQ4dsLyLlRUs\nWwa/+IX7zFxWs3C2+3J5cnNh0B3uM29ZzVtv6YZhu3Y5MHkooUtu2+zKfDY4z4YNIi+9JJKb67Qk\npVm6VKRHD5GdO52WxDr69sw7JUq7Lgckm3rSOPHAKcf69jzuiJxz5uj5b7nFkelt48cfRT74wNqq\nBtgUrhoTbNqk6767CSeWl06wahUMHw5btjgtSWm6dIHZs6F5c6clsY5gtaAO4HwJ6LL4S51nZ7nL\nvGU1LVtCnz5w2mn2z20UQwjcdRf84Q9OS1GaTz6Bpk1h40anJYksN96onYznnee0JKWJRsXslVwe\nv3lLctxj3ooUmzaBE80qjWIIgbFjdatDN9GkiQ7Xa9rUaUkiS2qqO9tLtm8Pzz3ntBTW47ZcnmCl\narZucn+pGqu4/3548EH75zWKIQQuu8x9d6yXXAJTpzqzzLSbd96BN95wWoqTFBXB9ddrx3i04s/l\nmawG04g9THEol8etHc7s4q9/hYkT7Z9XiQfXxOnp6ZJh4/oqL0+XB+jUyT1RQPn5kORYARF76d1b\n9x/+5hunJYkNsrKgbYsCLlffsLNROr+4rBbff51Lq/3LWSRd2bozkcaN7ZMnNxceGHyc5Z/v44O8\nHpzDup+PreEc+qbOpsvNDXl9knuqErgVpdR3IpJe2TizYgiBo0d1CejPPnNaEo0INGwII0Y4LYk9\nTJ/uLqVQUBCdPgY/gbk8GetrMX06rNjkXC0ot5m37OT4cf27s3Vr5WOtxCiGEGjUSHcUGzCg8rF2\ncOgQtGuR74meyFaQ7DLrwCOPQLt2TksROcrm8hQWwt69zteCCjRvublUjZXk5kLPnvYXyjSmJA8y\ndy5ce61+jObMTz/79+vs57594fLLnZZG9xjeuFFX94wFHn0U/v53OHbMuaY4fvNWV/UNSwrTGTCs\nFvM/y6X5HmfMW3aybBl07GiNP9GYkixmwwZ47TV3mBD+87k7yxREiuRknQW6YYPTkmh+/evYUQoA\n/fvDm29CcbFzMvjNW/V6dGHwQ7W4667oK3VeHl262B9kYhRDiCxcCL/7Heze7bQk8NHb7itTEElq\n1dIlxvv3d74UQn6+czXynSI9He64w9ny7n7z1rSZKYwbp38sY6XU+fr19t+UGsUQIv36wZ490KyZ\nvfMGi+POy4mdOG4/SsGSJTD1PWcrfS5erPMqFi50TgYn2L7d2WRKf6nzw4dP/YGMplLnwfDflP70\nk31zGsUQIrVraye0UvbOGyyOe29h7MRx+5k1Cx4a6rwJrVUreOklHbocS/ToAY+X20bLPrp3187Y\nWKJ/f+38P/NM++Y0iqEKTJsG//qXvXN6pUxBpDl8GA7tdt6E1qqV/oGsX98xERzh1VfhmWeclgKG\nDnVPdKBd1K6tw9PtvCk1UUlV4LrrICdHmxOc4G9jSnjtyT38mH/y1qFNSiYPPNeIPzwaXTr+tl7H\nmTErpdS+xokHWV1wFucmbmJPQd1Sx/r2PMH7n0Z+tbRxoy6c58ZS4Ibo5Z13dOOq228P7zwmKikC\nzJgBX3/t3Py7thbQSz5hEoNplRLdcdxuLIUgokuRuK2goh0cP67Do50Mvti3T4cuxyKTJtnb/zks\nxaCUqquUmqOU2ux7rFPOuOKA7m2zAva3Ukot873/fV8bUNdyxhlaazuBv+XoejrytxbjGPteI0Y1\nj96Wo240oZWU6LDNwYMjP5fbyM7WuTOff+6cDGPH6uKRBdF3H1Qpn3wC8+bZOGEoTRvK24BRwHDf\n8+HAX8sZl1PO/hlAP9/zN4ChoczrVKOeAwdEHntMZPFi++d+eOhxAZEbuuXJ9u16X06OyIC+urnK\nI8OcaZpiB2NGF0ublJ9KdYlpnZIpY0YXOy1azFBSIjJ/vsihQ87J8P33IpMnOzd/NIBNjXp6AW/5\nnr8F9A71jUopBVwF+NupV+n9TpCUpOOJV62yf+78vBJee7WEfy9MYfp0vS9W4rj9pRCmOFwKYfNm\nWLfOHUmOdqMU/OpXzpZAv+ACGDTIufmdZO9e+P3vodcNNuXxhKI9ytuAw2VeHypnXBGQASwFevv2\n1Qe2BIxpDqwJZV4nW3sWFTk2teTni3z1lfy8YogFMjNFUhPypXviAunY4pjMnCnSIS1HuifOl9SE\nfMnKsk+We+4RqV/fvvncxubNIm+8IVLswELt2DGRb74Rycuzf243sG+fSEKCXjDPnVv982DVikEp\nNVcptSbI1qsK+idNtCf8duAVpVQbIFjwVbn3Ykqp+5RSGUqpjOzs7CpMbS1O1YoBnXl6xRXQooVz\nMthNYKXPuUtq8cwzcN8jzpRCeOwxePdd26ZzHQsW6MYx27fbP/eyZbovilMRgU7ToAH838P25fFU\nqhhE5BoR6RRk+xTYq5RqAuB73FfOOTJ9j9uAhcAFwH6gtlKqhm9YMyCzAjkmiki6iKQ3aNCgCn+i\ntcybp/uw5ufbP/cXX8Dy5fbP6ySBlT4bN9bNcZo3t9+ElpsLL43Md0URP6fo0wd27NC5HHZz4YW6\nwujFF9s/t1v46t825vGEsqwobwNeprTzeVSQMXWAJDlpPtoMdPS9/oDSzudhoczrpClpxgyR9u1F\nduywf+6zzhLp3dv+eQ0iH3+sl/EzZzotiSEW6NszLzDWQkCkYY0Dkk09aZx44JRjfXuGFnyCTc7n\nl4BrlVKbgWt9r1FKpSul/FG3HYAMpdQPwALgJRHxt2B6HPiDUmoLUA+YFKY8EefWW3WVz7Q0++de\nuBBeftn+ed1GcbH9lT5nTNPL+JkfxEZF2/L49FNd6dZuZs2yv1mNkwQthVNkXx5PWIpBRA6IyNUi\n0s73eNC3P0NEhviefyMi54rIL3yPkwLev01EOotIWxG5VUQcMNBUj9xc+yt9NmkCbdvaN58bWbRI\n55MsXWrvvDvW6mX85pWxUdG2PN5+G0aPtnfOggJtxnrzTXvndRKn83hM5nM1eOIJuPtueyt9LlwI\nkyfrJKtYpl07nWBWt27lY6tLsIq2P27RFW1/3BI7FW2D8eabsGKFvXPWqAErV+o6SbGEky1NjWKo\nBrm5sH2LvZU+33kHnnrKucxrt9CokS7o1qFD5OZwYzkOt1C3rv6htpO4ON3BLJai8QJxIo8nxn9m\nqserr0JKib2VPidOhO++s2Uq1yOiE34ihdPLeDdz/Dg8/TTMmVP5WKv48kv47DP75nMT/lI46+jI\n6LRxjH3XplI4oXio3bbZHZUULEKgcWL4EQKG6vHSSyJK6aSnSGPKcZSmuFjkjDNEnnvOvjmvuUYk\nPd2++dyEvxTOwNvyJCdH7wunFA42RSXFBE6bFtatg+HD7e3g5Gauuw7GjbPH3+KWchxuIS5OF9R7\n6in75vz0U/jww8rHRSOBeTx+X4ItpXBC0R5u25zIY/Br6Q6p22UNHUvdQa7mHOmQur2UVreS6dN1\nOrwTuROxjL8cx9XxC6RlA2fLcbiNnByRgbefkNxcpyUxVAXMisFanIwQuO02OHZMZ/waNEeO6Cbp\nkcRfjmNNzS5ccnUtevfWvgcnynG4ie++g5tusicqb8UK+Otf4dChyM5jKI1RDFWkrGlhik2mhaQk\n+/tNu5kBA+DXv47sHP5lfNbhFCZO1PtipaJtReTlwarv7YnKW7RIm1HNd99eTGvPKpCVBW1bFHC5\n+oYf66eTdbQW9VNyOevIchZJV7buTKRxY2vnzM+HO+/UMdxXXWXtub3MokX6B6p7d6cliU0uP/cI\nV6yZwNedhrFo9RkRmSM3Fx68L58XRifRpElEpog5TGvPCBBY6XPJqlr06wdvfRhZ00JWlk7uOXDA\n8lN7mssvt0cpjB0LI0dGfh43Eyzhb+smnfC3dVPkEv6WLNHmqnXrKh9rsBabU1W8jd+0MHCwblLv\nT9G/8soUul1XwvKvrDcttGypG8QYTuX773Wy1XnnRW6OVatit8+wnxEvpLB6ZS6d933O+BODqUke\n+CynWQX1AMgllWHJU8ho1IMRL9SyZN45/ymiJid49+1krr7a/FTZiTElhcnevVCnju6VYLCXtDS9\ncnjvvcjOI2Js3Lm58MDg4yz/fB8f5PXgHE7exq/hHPqmzqbLzQ15fZJ1ARgXn3WEazZPYHbaMFbt\niIy5KtYwpiQbmD8fGjeGb76J3Bx33QW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5Gsp1jCSD0XeXwbDjeoXy9/88xvddOoL+btmC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\noxgMBoPBUAqjGAwGg8FQiv8HEdrtxuo+oLAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x829e908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line=plt.plot(x,y)\n",
    "plt.setp(line,color='b',linestyle=':',marker='*',markerfacecolor='r',markersize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc5a1c18>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tWkr3cv4886uvyjgvW5Z54kTmJ5+U/4sWlefHjqmWMqoIVrGTHBsZRLQCwJPM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\ndgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc559278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 211 表示一会要画的图是2行1列的图 最后一个1表示的是子图当中的第1个图\n",
    "plt.subplot(211)\n",
    "plt.plot(x,y,'r-')\n",
    "\n",
    "# 212 表示一会要画的图是2行一列的 最后一个1表示的是子图当中的第2个图\n",
    "plt.subplot(212)\n",
    "plt.plot(x,y,'b:')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc656c50>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5YqtePe9jmKiXlJ4WA6B/ZbtzJ4dC+p1NppLcNmzwXkdZYirJzW9yG8C+jObN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\nWMVgsVgslkpYxWCxWCyWSvwfJTqXO+K+KLIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc615eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 211 表示一会要画的图是2行1列的图 最后一个1表示的是子图当中的第1个图\n",
    "plt.subplot(121)\n",
    "plt.plot(x,y,'r-')\n",
    "\n",
    "# 212 表示一会要画的图是2行一列的 最后一个1表示的是子图当中的第2个图\n",
    "plt.subplot(122)\n",
    "plt.plot(x,y,'b:')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xcb4a358>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q0iXGaigmMMMuIvLgg/wavXvzs1kzkVdf1Ve+Tv77vykjIHLppfJ/XnE6UlHB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\nBPPnB7solU7y8jg55UtPG82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88Y+UZ968uucqK0Xy80UuuEDk9Gn7stXm2DGR884Tuekm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\njpMYntgdx3ES4y/kMQP5yQ4uXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc8a2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(321)\n",
    "plt.plot(x,y,color='r')\n",
    "plt.subplot(322)\n",
    "plt.plot(x,y,color='b')\n",
    "plt.subplot(323)\n",
    "plt.plot(x,y,color='r')\n",
    "plt.subplot(324)\n",
    "plt.plot(x,y,color='r')\n",
    "plt.subplot(325)\n",
    "plt.plot(x,y,color='b')\n",
    "plt.subplot(326)\n",
    "plt.plot(x,y,color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 给图上加上注释"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-1.5,0.5,'guochao')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ff/45ALt27eLvv/+mVq1atG3blo8++uiKAOeFCxcyHK9m7SB6PxFEePh5brqp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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf677e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,color='b',linestyle=':',marker='*',markerfacecolor='r',markersize=12)\n",
    "plt.xlabel('x:---',fontsize=16)\n",
    "plt.ylabel('y:---',fontsize=16)\n",
    "\n",
    "#添加常用标注\n",
    "plt.title('test line',fontsize=16)#添加主题\n",
    "plt.text(0,0,'key',fontsize=16)#图上坐标点标注\n",
    "plt.grid(True)#显示格子\n",
    "plt.annotate('guochao',xy=(-5,1),xytext=(-1.5,0.5),arrowprops=dict(facecolor='red',shrink=0.05,headwidth=20,headlength=20))#精细标注图上位置\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 如何隐藏x，y坐标轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x=np.arange(10)\n",
    "y=np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HgIgYZNKrY/mNWJ6lQEOHEk9PDMIrd4fB1pLfhkQAg0x60trdj9d352LnT7UI8xyCrY+M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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf96bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.gca()#获取坐标抽信息\n",
    "plt.plot(x,y)\n",
    "#获取坐标系\n",
    "fig.axes.get_xaxis().set_visible(False)# 设置隐藏坐标\n",
    "fig.axes.get_yaxis().set_visible(False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 子图柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  4.11204485e-01  -4.24465766e-01   5.01383733e-01   1.75680117e+00\n",
      "   9.57393567e-01   3.15065988e-01  -9.92760092e-01   4.54073365e-01\n",
      "   1.29527150e+00   1.41225016e-01   5.64220175e-01   5.94696217e-01\n",
      "  -3.13027906e-01   9.14731605e-01   4.54921364e-01  -1.39915178e+00\n",
      "  -3.04975456e-01  -5.74836556e-01  -1.36752092e+00  -2.18673468e-01\n",
      "   1.77700387e+00   7.56812926e-01  -1.82890250e-02  -9.24892799e-01\n",
      "  -3.21780619e-01   4.78572140e-01  -1.24144979e+00  -1.24818237e-02\n",
      "   2.77276917e+00  -2.04017254e+00   9.70047551e-01  -9.23506925e-03\n",
      "  -8.97812639e-01   3.67208720e-01   7.63598795e-01   4.78194793e-01\n",
      "   2.00138399e+00   1.17115310e-01   1.19577502e+00   2.39192454e-01\n",
      "   1.85132745e+00   1.12252650e+00   1.82886525e-01  -1.17041773e+00\n",
      "  -1.30019841e+00   1.39218387e+00   9.23197767e-01  -3.35772797e-01\n",
      "  -2.61601782e-01   1.02646088e+00   1.32947694e-01   3.33728597e-01\n",
      "   8.62561124e-01   1.02389967e+00  -3.55724760e-01  -9.17307700e-01\n",
      "  -9.45614125e-01   1.65518956e-01  -7.88446016e-01   1.02159856e+00\n",
      "  -5.90265648e-01  -4.50502988e-01   4.04586504e-01  -5.32595506e-01\n",
      "  -1.95045573e+00   1.70724970e-01   6.27569824e-01   1.21192854e-01\n",
      "  -3.87427117e-01  -7.78447029e-01  -1.03551676e+00  -5.47618434e-01\n",
      "   6.05227249e-01  -8.37705717e-01   3.14969764e-01  -1.61352475e-02\n",
      "   1.43820645e+00   4.34033935e-01   8.76615743e-01   2.11932219e+00\n",
      "  -7.79615593e-01  -8.92200131e-01   1.84318304e+00   7.18706984e-01\n",
      "   1.63116640e+00   1.50535762e-02  -1.79150114e-01  -6.62380432e-01\n",
      "   6.78772334e-01  -1.01838075e+00  -3.14782172e-01   2.27678205e-02\n",
      "  -6.37946775e-01   3.57025709e-01  -2.90610152e-01  -5.03501026e-01\n",
      "  -6.33881981e-01   7.12522091e-01   7.11415021e-01  -1.04505590e+00\n",
      "   2.47227797e+00  -6.48220225e-01   5.55415919e-01  -4.57659183e-01\n",
      "   5.59866666e-01   7.93731956e-01   1.30111684e+00   3.32875629e-01\n",
      "  -5.65580319e-01  -3.21333848e-01   2.00061247e+00  -1.41828286e+00\n",
      "  -1.19555440e+00   1.32014916e+00   4.69472003e-02  -1.49923375e+00\n",
      "   1.36102659e+00   1.74628184e+00  -6.99200983e-01  -8.42470175e-02\n",
      "   1.23368284e+00  -1.56583722e+00   4.42448510e-01   1.22738372e+00\n",
      "   1.28548207e+00   1.25728424e+00  -4.76085599e-01   4.82175735e-01\n",
      "   3.90282864e-01  -7.79208498e-01   1.58724582e+00  -7.44575350e-01\n",
      "   7.55184277e-02  -2.36030425e+00  -2.14602122e+00   1.80194751e+00\n",
      "   2.22118906e+00  -1.64192816e-01   7.27781247e-01  -6.36220624e-01\n",
      "   1.44571340e+00   1.56896455e+00   2.13616496e-01  -1.42488952e-01\n",
      "   5.50798444e-01   7.01776048e-01  -6.35794537e-01   2.95631920e-01\n",
      "   1.45653865e+00  -1.21710607e+00   5.94559640e-01  -7.55326928e-02\n",
      "   5.17221565e-01   2.04888572e-02   2.17734185e-02   1.42297983e+00\n",
      "  -1.23463796e+00  -1.13142193e+00   8.48205799e-01   5.02688560e-01\n",
      "  -7.76684381e-01   3.77633156e-01  -2.34594678e-01   1.09295650e+00\n",
      "   6.30219079e-01  -1.36842311e+00  -1.75299480e+00   5.39174780e-01\n",
      "   6.06892032e-01  -6.91669259e-01   6.52219899e-01  -1.74189149e+00\n",
      "   2.52357641e+00  -2.53672785e-01  -8.86577129e-01  -1.29591300e-02\n",
      "  -3.54742864e-01  -4.93654942e-01   6.60463712e-01   1.57260913e+00\n",
      "   4.33535197e-01   8.69319075e-01  -1.42255605e+00   2.38466833e-01\n",
      "   2.66848586e-01   6.33248128e-01   1.11423475e+00  -1.08209300e+00\n",
      "   6.37275152e-01  -8.84428377e-01  -2.38278396e-01  -4.14600555e-01\n",
      "   5.53936726e-02  -2.76751094e-01  -4.91805747e-01  -1.48573040e+00\n",
      "   2.03130135e-01   4.28182936e-01   1.74199675e+00  -1.53003402e+00\n",
      "   5.26798201e-02  -7.54444513e-01   1.20040216e-01  -1.64402294e+00\n",
      "  -6.70075888e-01   9.07546278e-01   3.45150244e-01   5.89538327e-01\n",
      "  -2.06591050e+00   6.14963691e-01   2.47083906e-01  -1.39189836e+00\n",
      "  -1.31395762e+00  -2.55040209e-01  -1.32005644e-02   1.40148520e+00\n",
      "   1.85979618e+00   7.66331094e-01  -6.49186190e-02  -1.28199314e+00\n",
      "   5.55146180e-01  -1.83909596e-01   3.36714941e-01  -2.72495447e-03\n",
      "  -4.60783626e-01  -5.34031006e-01  -4.91398549e-01  -3.78151327e-01\n",
      "   6.18608113e-01   2.20363780e-01   5.07880740e-01   7.19788169e-01\n",
      "  -1.46884747e+00   1.97989647e+00   2.57959474e+00  -5.40406659e-01\n",
      "  -1.60920412e+00   1.46954403e-01  -1.83906066e+00   7.10608964e-02\n",
      "   3.32769114e+00   1.16995493e-01   1.90952908e-01   9.37614996e-01\n",
      "  -2.70693316e-01   7.76037634e-01  -4.35486295e-01  -1.77860727e+00\n",
      "   8.85933619e-01  -3.45246598e-01  -1.31493956e-01  -1.21591834e+00\n",
      "   4.16681614e-02  -1.30075353e+00  -1.52211745e+00   1.01381868e+00\n",
      "  -8.24642874e-01  -5.72295760e-01  -7.68295275e-02   3.22378914e-01\n",
      "  -2.04677586e+00   1.30243950e+00   2.09520580e-01  -3.02265914e-01\n",
      "   9.24131863e-01  -9.61749557e-01  -1.05498586e+00   7.62804269e-01\n",
      "   1.07321377e+00  -3.39903459e-01   3.35691824e-01  -2.29564713e-01\n",
      "   1.76077675e+00   9.76268592e-01  -6.23056913e-01   1.42894362e+00\n",
      "   1.08232298e+00  -1.21341344e+00  -8.13597969e-02   2.25715717e-01\n",
      "  -7.05254806e-01   7.84405640e-01   2.00067207e+00   3.29270803e-01\n",
      "   2.32932848e-01   9.86582272e-01   2.96883350e-01  -5.25206205e-01\n",
      "  -3.04357997e-01   1.53611321e+00   6.00948418e-01   8.64694383e-01\n",
      "  -2.04178230e+00   1.30937967e+00   1.17270516e+00   6.13914170e-01\n",
      "   1.21044478e-01  -1.18224929e+00  -1.61731655e-01   6.22145409e-01]\n",
      "[-3.5 -3.  -2.5 -2.  -1.5 -1.  -0.5  0.   0.5  1.   1.5  2.   2.5  3.   3.5\n",
      "  4. ]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "x = np.random.normal(loc = 0.0,scale=1.0,size=300)\n",
    "width = 0.5\n",
    "bins = np.arange(math.floor(x.min())-width,math.ceil(x.max())+width,width)\n",
    "print(x)\n",
    "print(bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   0.,   6.,  10.,  27.,  39.,  52.,  58.,  55.,  28.,  15.,\n",
       "          6.,   3.,   1.,   0.]),\n",
       " array([-3.5, -3. , -2.5, -2. , -1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,\n",
       "         2. ,  2.5,  3. ,  3.5,  4. ]),\n",
       " <a list of 15 Patch objects>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NOshqJ8hbU3wMGHtu8Qw9ArytqhaAC4A3Duzf8CHgoqp6HvB8YE+SC2ac6XjeAhyc9pNs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1bf940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=plt.subplot(111)\n",
    "ax.spines['top'].set_visible(False) #顶部和右边的边框\n",
    "ax.spines['right'].set_visible(False)\n",
    "#plt.tick_params()里面的参数设置两个坐标轴的刻度的大小\n",
    "#plt.tick_params(axis=\"both\",labelsize=10)\n",
    "plt.tick_params(bottom='off',top='off',left ='off',right='off')\n",
    "plt.grid(True)\n",
    "plt.hist(x,alpha = 0.6,bins =bins)\n",
    "#默认情况按照高斯分布进行画图\n",
    "#http://blog.csdn.net/u013571243/article/details/48998619"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#我们也可以使用matplotlib直接画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RcParams({'_internal.classic_mode': False,\n",
       "          'agg.path.chunksize': 0,\n",
       "          'animation.avconv_args': [],\n",
       "          'animation.avconv_path': 'avconv',\n",
       "          'animation.bitrate': -1,\n",
       "          'animation.codec': 'h264',\n",
       "          'animation.convert_args': [],\n",
       "          'animation.convert_path': 'convert',\n",
       "          'animation.embed_limit': 20.0,\n",
       "          'animation.ffmpeg_args': [],\n",
       "          'animation.ffmpeg_path': 'ffmpeg',\n",
       "          'animation.frame_format': 'png',\n",
       "          'animation.html': 'none',\n",
       "          'animation.html_args': [],\n",
       "          'animation.mencoder_args': [],\n",
       "          'animation.mencoder_path': 'mencoder',\n",
       "          'animation.writer': 'ffmpeg',\n",
       "          'axes.autolimit_mode': 'data',\n",
       "          'axes.axisbelow': 'line',\n",
       "          'axes.edgecolor': 'k',\n",
       "          'axes.facecolor': 'w',\n",
       "          'axes.formatter.limits': [-7, 7],\n",
       "          'axes.formatter.min_exponent': 0,\n",
       "          'axes.formatter.offset_threshold': 4,\n",
       "          'axes.formatter.use_locale': False,\n",
       "          'axes.formatter.use_mathtext': False,\n",
       "          'axes.formatter.useoffset': True,\n",
       "          'axes.grid': False,\n",
       "          'axes.grid.axis': 'both',\n",
       "          'axes.grid.which': 'major',\n",
       "          'axes.hold': None,\n",
       "          'axes.labelcolor': 'k',\n",
       "          'axes.labelpad': 4.0,\n",
       "          'axes.labelsize': 'medium',\n",
       "          'axes.labelweight': 'normal',\n",
       "          'axes.linewidth': 0.8,\n",
       "          'axes.prop_cycle': cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']),\n",
       "          'axes.spines.bottom': True,\n",
       "          'axes.spines.left': True,\n",
       "          'axes.spines.right': True,\n",
       "          'axes.spines.top': True,\n",
       "          'axes.titlepad': 6.0,\n",
       "          'axes.titlesize': 'large',\n",
       "          'axes.titleweight': 'normal',\n",
       "          'axes.unicode_minus': True,\n",
       "          'axes.xmargin': 0.05,\n",
       "          'axes.ymargin': 0.05,\n",
       "          'axes3d.grid': True,\n",
       "          'backend': 'Qt5Agg',\n",
       "          'backend.qt4': 'PyQt4',\n",
       "          'backend.qt5': 'PyQt5',\n",
       "          'backend_fallback': True,\n",
       "          'boxplot.bootstrap': None,\n",
       "          'boxplot.boxprops.color': 'k',\n",
       "          'boxplot.boxprops.linestyle': '-',\n",
       "          'boxplot.boxprops.linewidth': 1.0,\n",
       "          'boxplot.capprops.color': 'k',\n",
       "          'boxplot.capprops.linestyle': '-',\n",
       "          'boxplot.capprops.linewidth': 1.0,\n",
       "          'boxplot.flierprops.color': 'k',\n",
       "          'boxplot.flierprops.linestyle': 'none',\n",
       "          'boxplot.flierprops.linewidth': 1.0,\n",
       "          'boxplot.flierprops.marker': 'o',\n",
       "          'boxplot.flierprops.markeredgecolor': 'k',\n",
       "          'boxplot.flierprops.markerfacecolor': 'none',\n",
       "          'boxplot.flierprops.markersize': 6.0,\n",
       "          'boxplot.meanline': False,\n",
       "          'boxplot.meanprops.color': 'C2',\n",
       "          'boxplot.meanprops.linestyle': '--',\n",
       "          'boxplot.meanprops.linewidth': 1.0,\n",
       "          'boxplot.meanprops.marker': '^',\n",
       "          'boxplot.meanprops.markeredgecolor': 'C2',\n",
       "          'boxplot.meanprops.markerfacecolor': 'C2',\n",
       "          'boxplot.meanprops.markersize': 6.0,\n",
       "          'boxplot.medianprops.color': 'C1',\n",
       "          'boxplot.medianprops.linestyle': '-',\n",
       "          'boxplot.medianprops.linewidth': 1.0,\n",
       "          'boxplot.notch': False,\n",
       "          'boxplot.patchartist': False,\n",
       "          'boxplot.showbox': True,\n",
       "          'boxplot.showcaps': True,\n",
       "          'boxplot.showfliers': True,\n",
       "          'boxplot.showmeans': False,\n",
       "          'boxplot.vertical': True,\n",
       "          'boxplot.whiskerprops.color': 'k',\n",
       "          'boxplot.whiskerprops.linestyle': '-',\n",
       "          'boxplot.whiskerprops.linewidth': 1.0,\n",
       "          'boxplot.whiskers': 1.5,\n",
       "          'contour.corner_mask': True,\n",
       "          'contour.negative_linestyle': 'dashed',\n",
       "          'datapath': 'D:\\\\sorfware_install\\\\python_install\\\\lib\\\\site-packages\\\\matplotlib\\\\mpl-data',\n",
       "          'date.autoformatter.day': '%Y-%m-%d',\n",
       "          'date.autoformatter.hour': '%m-%d %H',\n",
       "          'date.autoformatter.microsecond': '%M:%S.%f',\n",
       "          'date.autoformatter.minute': '%d %H:%M',\n",
       "          'date.autoformatter.month': '%Y-%m',\n",
       "          'date.autoformatter.second': '%H:%M:%S',\n",
       "          'date.autoformatter.year': '%Y',\n",
       "          'docstring.hardcopy': False,\n",
       "          'errorbar.capsize': 0.0,\n",
       "          'examples.directory': '',\n",
       "          'figure.autolayout': False,\n",
       "          'figure.dpi': 100.0,\n",
       "          'figure.edgecolor': 'w',\n",
       "          'figure.facecolor': 'w',\n",
       "          'figure.figsize': [6.4, 4.8],\n",
       "          'figure.frameon': True,\n",
       "          'figure.max_open_warning': 20,\n",
       "          'figure.subplot.bottom': 0.11,\n",
       "          'figure.subplot.hspace': 0.2,\n",
       "          'figure.subplot.left': 0.125,\n",
       "          'figure.subplot.right': 0.9,\n",
       "          'figure.subplot.top': 0.88,\n",
       "          'figure.subplot.wspace': 0.2,\n",
       "          'figure.titlesize': 'large',\n",
       "          'figure.titleweight': 'normal',\n",
       "          'font.cursive': ['Apple Chancery',\n",
       "                           'Textile',\n",
       "                           'Zapf Chancery',\n",
       "                           'Sand',\n",
       "                           'Script MT',\n",
       "                           'Felipa',\n",
       "                           'cursive'],\n",
       "          'font.family': ['sans-serif'],\n",
       "          'font.fantasy': ['Comic Sans MS',\n",
       "                           'Chicago',\n",
       "                           'Charcoal',\n",
       "                           'ImpactWestern',\n",
       "                           'Humor Sans',\n",
       "                           'xkcd',\n",
       "                           'fantasy'],\n",
       "          'font.monospace': ['DejaVu Sans Mono',\n",
       "                             'Bitstream Vera Sans Mono',\n",
       "                             'Computer Modern Typewriter',\n",
       "                             'Andale Mono',\n",
       "                             'Nimbus Mono L',\n",
       "                             'Courier New',\n",
       "                             'Courier',\n",
       "                             'Fixed',\n",
       "                             'Terminal',\n",
       "                             'monospace'],\n",
       "          'font.sans-serif': ['DejaVu Sans',\n",
       "                              'Bitstream Vera Sans',\n",
       "                              'Computer Modern Sans Serif',\n",
       "                              'Lucida Grande',\n",
       "                              'Verdana',\n",
       "                              'Geneva',\n",
       "                              'Lucid',\n",
       "                              'Arial',\n",
       "                              'Helvetica',\n",
       "                              'Avant Garde',\n",
       "                              'sans-serif'],\n",
       "          'font.serif': ['DejaVu Serif',\n",
       "                         'Bitstream Vera Serif',\n",
       "                         'Computer Modern Roman',\n",
       "                         'New Century Schoolbook',\n",
       "                         'Century Schoolbook L',\n",
       "                         'Utopia',\n",
       "                         'ITC Bookman',\n",
       "                         'Bookman',\n",
       "                         'Nimbus Roman No9 L',\n",
       "                         'Times New Roman',\n",
       "                         'Times',\n",
       "                         'Palatino',\n",
       "                         'Charter',\n",
       "                         'serif'],\n",
       "          'font.size': 10.0,\n",
       "          'font.stretch': 'normal',\n",
       "          'font.style': 'normal',\n",
       "          'font.variant': 'normal',\n",
       "          'font.weight': 'normal',\n",
       "          'grid.alpha': 1.0,\n",
       "          'grid.color': '#b0b0b0',\n",
       "          'grid.linestyle': '-',\n",
       "          'grid.linewidth': 0.8,\n",
       "          'hatch.color': 'k',\n",
       "          'hatch.linewidth': 1.0,\n",
       "          'hist.bins': 10,\n",
       "          'image.aspect': 'equal',\n",
       "          'image.cmap': 'viridis',\n",
       "          'image.composite_image': True,\n",
       "          'image.interpolation': 'nearest',\n",
       "          'image.lut': 256,\n",
       "          'image.origin': 'upper',\n",
       "          'image.resample': True,\n",
       "          'interactive': False,\n",
       "          'keymap.all_axes': ['a'],\n",
       "          'keymap.back': ['left', 'c', 'backspace'],\n",
       "          'keymap.forward': ['right', 'v'],\n",
       "          'keymap.fullscreen': ['f', 'ctrl+f'],\n",
       "          'keymap.grid': ['g'],\n",
       "          'keymap.grid_minor': ['G'],\n",
       "          'keymap.home': ['h', 'r', 'home'],\n",
       "          'keymap.pan': ['p'],\n",
       "          'keymap.quit': ['ctrl+w', 'cmd+w', 'q'],\n",
       "          'keymap.quit_all': ['W', 'cmd+W', 'Q'],\n",
       "          'keymap.save': ['s', 'ctrl+s'],\n",
       "          'keymap.xscale': ['k', 'L'],\n",
       "          'keymap.yscale': ['l'],\n",
       "          'keymap.zoom': ['o'],\n",
       "          'legend.borderaxespad': 0.5,\n",
       "          'legend.borderpad': 0.4,\n",
       "          'legend.columnspacing': 2.0,\n",
       "          'legend.edgecolor': '0.8',\n",
       "          'legend.facecolor': 'inherit',\n",
       "          'legend.fancybox': True,\n",
       "          'legend.fontsize': 'medium',\n",
       "          'legend.framealpha': 0.8,\n",
       "          'legend.frameon': True,\n",
       "          'legend.handleheight': 0.7,\n",
       "          'legend.handlelength': 2.0,\n",
       "          'legend.handletextpad': 0.8,\n",
       "          'legend.labelspacing': 0.5,\n",
       "          'legend.loc': 'best',\n",
       "          'legend.markerscale': 1.0,\n",
       "          'legend.numpoints': 1,\n",
       "          'legend.scatterpoints': 1,\n",
       "          'legend.shadow': False,\n",
       "          'lines.antialiased': True,\n",
       "          'lines.color': 'C0',\n",
       "          'lines.dash_capstyle': 'butt',\n",
       "          'lines.dash_joinstyle': 'round',\n",
       "          'lines.dashdot_pattern': [6.4, 1.6, 1.0, 1.6],\n",
       "          'lines.dashed_pattern': [3.7, 1.6],\n",
       "          'lines.dotted_pattern': [1.0, 1.65],\n",
       "          'lines.linestyle': '-',\n",
       "          'lines.linewidth': 1.5,\n",
       "          'lines.marker': 'None',\n",
       "          'lines.markeredgewidth': 1.0,\n",
       "          'lines.markersize': 6.0,\n",
       "          'lines.scale_dashes': True,\n",
       "          'lines.solid_capstyle': 'projecting',\n",
       "          'lines.solid_joinstyle': 'round',\n",
       "          'markers.fillstyle': 'full',\n",
       "          'mathtext.bf': 'sans:bold',\n",
       "          'mathtext.cal': 'cursive',\n",
       "          'mathtext.default': 'it',\n",
       "          'mathtext.fallback_to_cm': True,\n",
       "          'mathtext.fontset': 'dejavusans',\n",
       "          'mathtext.it': 'sans:italic',\n",
       "          'mathtext.rm': 'sans',\n",
       "          'mathtext.sf': 'sans',\n",
       "          'mathtext.tt': 'monospace',\n",
       "          'nbagg.transparent': True,\n",
       "          'patch.antialiased': True,\n",
       "          'patch.edgecolor': 'k',\n",
       "          'patch.facecolor': 'C0',\n",
       "          'patch.force_edgecolor': False,\n",
       "          'patch.linewidth': 1.0,\n",
       "          'path.effects': [],\n",
       "          'path.simplify': True,\n",
       "          'path.simplify_threshold': 0.1111111111111111,\n",
       "          'path.sketch': None,\n",
       "          'path.snap': True,\n",
       "          'pdf.compression': 6,\n",
       "          'pdf.fonttype': 3,\n",
       "          'pdf.inheritcolor': False,\n",
       "          'pdf.use14corefonts': False,\n",
       "          'pgf.debug': False,\n",
       "          'pgf.preamble': [],\n",
       "          'pgf.rcfonts': True,\n",
       "          'pgf.texsystem': 'xelatex',\n",
       "          'plugins.directory': '.matplotlib_plugins',\n",
       "          'polaraxes.grid': True,\n",
       "          'ps.distiller.res': 6000,\n",
       "          'ps.fonttype': 3,\n",
       "          'ps.papersize': 'letter',\n",
       "          'ps.useafm': False,\n",
       "          'ps.usedistiller': False,\n",
       "          'savefig.bbox': None,\n",
       "          'savefig.directory': '~',\n",
       "          'savefig.dpi': 'figure',\n",
       "          'savefig.edgecolor': 'w',\n",
       "          'savefig.facecolor': 'w',\n",
       "          'savefig.format': 'png',\n",
       "          'savefig.frameon': True,\n",
       "          'savefig.jpeg_quality': 95,\n",
       "          'savefig.orientation': 'portrait',\n",
       "          'savefig.pad_inches': 0.1,\n",
       "          'savefig.transparent': False,\n",
       "          'scatter.marker': 'o',\n",
       "          'svg.fonttype': 'path',\n",
       "          'svg.hashsalt': None,\n",
       "          'svg.image_inline': True,\n",
       "          'text.antialiased': True,\n",
       "          'text.color': 'k',\n",
       "          'text.hinting': 'auto',\n",
       "          'text.hinting_factor': 8,\n",
       "          'text.latex.preamble': [],\n",
       "          'text.latex.preview': False,\n",
       "          'text.latex.unicode': False,\n",
       "          'text.usetex': False,\n",
       "          'timezone': 'UTC',\n",
       "          'tk.window_focus': False,\n",
       "          'toolbar': 'toolbar2',\n",
       "          'verbose.fileo': 'sys.stdout',\n",
       "          'verbose.level': 'silent',\n",
       "          'webagg.open_in_browser': True,\n",
       "          'webagg.port': 8988,\n",
       "          'webagg.port_retries': 50,\n",
       "          'xtick.alignment': 'center',\n",
       "          'xtick.bottom': True,\n",
       "          'xtick.color': 'k',\n",
       "          'xtick.direction': 'out',\n",
       "          'xtick.labelsize': 'medium',\n",
       "          'xtick.major.bottom': True,\n",
       "          'xtick.major.pad': 3.5,\n",
       "          'xtick.major.size': 3.5,\n",
       "          'xtick.major.top': True,\n",
       "          'xtick.major.width': 0.8,\n",
       "          'xtick.minor.bottom': True,\n",
       "          'xtick.minor.pad': 3.4,\n",
       "          'xtick.minor.size': 2.0,\n",
       "          'xtick.minor.top': True,\n",
       "          'xtick.minor.visible': False,\n",
       "          'xtick.minor.width': 0.6,\n",
       "          'xtick.top': False,\n",
       "          'ytick.alignment': 'center_baseline',\n",
       "          'ytick.color': 'k',\n",
       "          'ytick.direction': 'out',\n",
       "          'ytick.labelsize': 'medium',\n",
       "          'ytick.left': True,\n",
       "          'ytick.major.left': True,\n",
       "          'ytick.major.pad': 3.5,\n",
       "          'ytick.major.right': True,\n",
       "          'ytick.major.size': 3.5,\n",
       "          'ytick.major.width': 0.8,\n",
       "          'ytick.minor.left': True,\n",
       "          'ytick.minor.pad': 3.4,\n",
       "          'ytick.minor.right': True,\n",
       "          'ytick.minor.size': 2.0,\n",
       "          'ytick.minor.visible': False,\n",
       "          'ytick.minor.width': 0.6,\n",
       "          'ytick.right': False})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpl.rc_params() #获取mpl画图是初始化参数【默认值】，我们可以通过修改配置参数调整画图式样 全局变量设置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 全局变量设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['axes.titlesize']=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'guochao'),\n",
       " Text(0,0,'guochao'),\n",
       " Text(0,0,'guochao'),\n",
       " Text(0,0,'guochao'),\n",
       " Text(0,0,'guochao'),\n",
       " Text(0,0,'guochao'),\n",
       " Text(0,0,'guochao')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc312128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(10)\n",
    "y = range(10)\n",
    "\n",
    "labels = ['guochao' for i in range(10)]\n",
    "fig,ax = plt.subplots()\n",
    "plt.plot(x,y)\n",
    "plt.title('guochao')\n",
    "#### 重点\n",
    "ax.set_xticklabels(labels,rotation = 45,horizontalalignment='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on list object:\n",
      "\n",
      "class list(object)\n",
      " |  list() -> new empty list\n",
      " |  list(iterable) -> new list initialized from iterable's items\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __add__(self, value, /)\n",
      " |      Return self+value.\n",
      " |  \n",
      " |  __contains__(self, key, /)\n",
      " |      Return key in self.\n",
      " |  \n",
      " |  __delitem__(self, key, /)\n",
      " |      Delete self[key].\n",
      " |  \n",
      " |  __eq__(self, value, /)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __ge__(self, value, /)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __getitem__(...)\n",
      " |      x.__getitem__(y) <==> x[y]\n",
      " |  \n",
      " |  __gt__(self, value, /)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __iadd__(self, value, /)\n",
      " |      Implement self+=value.\n",
      " |  \n",
      " |  __imul__(self, value, /)\n",
      " |      Implement self*=value.\n",
      " |  \n",
      " |  __init__(self, /, *args, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __iter__(self, /)\n",
      " |      Implement iter(self).\n",
      " |  \n",
      " |  __le__(self, value, /)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __len__(self, /)\n",
      " |      Return len(self).\n",
      " |  \n",
      " |  __lt__(self, value, /)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __mul__(self, value, /)\n",
      " |      Return self*value.n\n",
      " |  \n",
      " |  __ne__(self, value, /)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __reversed__(...)\n",
      " |      L.__reversed__() -- return a reverse iterator over the list\n",
      " |  \n",
      " |  __rmul__(self, value, /)\n",
      " |      Return self*value.\n",
      " |  \n",
      " |  __setitem__(self, key, value, /)\n",
      " |      Set self[key] to value.\n",
      " |  \n",
      " |  __sizeof__(...)\n",
      " |      L.__sizeof__() -- size of L in memory, in bytes\n",
      " |  \n",
      " |  append(...)\n",
      " |      L.append(object) -> None -- append object to end\n",
      " |  \n",
      " |  clear(...)\n",
      " |      L.clear() -> None -- remove all items from L\n",
      " |  \n",
      " |  copy(...)\n",
      " |      L.copy() -> list -- a shallow copy of L\n",
      " |  \n",
      " |  count(...)\n",
      " |      L.count(value) -> integer -- return number of occurrences of value\n",
      " |  \n",
      " |  extend(...)\n",
      " |      L.extend(iterable) -> None -- extend list by appending elements from the iterable\n",
      " |  \n",
      " |  index(...)\n",
      " |      L.index(value, [start, [stop]]) -> integer -- return first index of value.\n",
      " |      Raises ValueError if the value is not present.\n",
      " |  \n",
      " |  insert(...)\n",
      " |      L.insert(index, object) -- insert object before index\n",
      " |  \n",
      " |  pop(...)\n",
      " |      L.pop([index]) -> item -- remove and return item at index (default last).\n",
      " |      Raises IndexError if list is empty or index is out of range.\n",
      " |  \n",
      " |  remove(...)\n",
      " |      L.remove(value) -> None -- remove first occurrence of value.\n",
      " |      Raises ValueError if the value is not present.\n",
      " |  \n",
      " |  reverse(...)\n",
      " |      L.reverse() -- reverse *IN PLACE*\n",
      " |  \n",
      " |  sort(...)\n",
      " |      L.sort(key=None, reverse=False) -> None -- stable sort *IN PLACE*\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __hash__ = None\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(fig.axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xc3f2470>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c7Fd070/w7T32ztYbYPRj0tnqApzsp0iIpmlr3lamrZhGSXUJT5//NOPbjje7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\n6IUQopGToBdCiEZOgl4IIRq5/wdw4BYi2/KGKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3f2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#loc(设置图例显示的位置)http://blog.csdn.net/you_are_my_dream/article/details/53440964\n",
    "x=np.arange(10)\n",
    "for i in range(1,4):\n",
    "    plt.plot(x,i*x**2,label='Group-%d'%i)\n",
    "plt.legend(loc='best')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15089dd8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gIAwumgN9Lpe2eBPJzFghXJzFZuGl9S/x3pb36BfTj+dHPU90oItumbfzB1gw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\n9EII4eH+H1ZainzIzoeTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1234b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.subplot(111)\n",
    "x = np.arange(10)\n",
    "for i in range(1,4):\n",
    "    plt.plot(x,i*x**2,label = 'Group %d'%i)\n",
    "ax.legend(loc='upper center',bbox_to_anchor = (0.5,1.15) ,ncol=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x118d8978>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s2YFlPLz2YXzcfHjn/Hda7oootRXwyzOwdh54+sOlC6DvVXJtU7Qq0mFIiBbO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf59a7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.subplot(111)\n",
    "x = np.arange(10)\n",
    "for i in range(1,4):\n",
    "    plt.plot(x,i*x**2,label = 'Group %d'%i)\n",
    "ax.legend(loc='upper center',bbox_to_anchor = (1.15,1) ,ncol=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xf48a240>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rUSHqwmwxM3/nfKavnU5nv84smrjIcUMejOGaumwXNlXfoF8O3GL9/hbguwrb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\nawaBze8BYAgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf7dfc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "for i in range(1,4):\n",
    "    plt.plot(x,i*x**2,label = 'Group %d'%i,marker='o')\n",
    "plt.legend(loc='upper right',framealpha = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
